Efficient Multi-Modal Planning with Reward-Guided Preference Optimization for Autonomous Driving
cs.RO, cs.AI
Submitted: 2026-09-30
Updated: 2026-09-30
Terminology
Sources
- VADv2: End-to-End Vectorized Autonomous Driving via Probabilistic Planning
- ChauffeurNet: Learning to Drive by Imitating the Best and Synthesizing the Worst
- RAD: Training an End-to-End Driving Policy via Large-Scale 3DGS-based Reinforcement Learning
- Hydra-MDP: End-to-end Multimodal Planning with Multi-target Hydra-Distillation
- End-to-End Driving with Online Trajectory Evaluation via BEV World Model
- A Comprehensive Survey of Direct Preference Optimization: Datasets, Theories, Variants, and Applications
- DriveDPO: Policy Learning via Safety DPO For End-to-End Autonomous Driving
- AlphaDrive: Unleashing the Power of VLMs in Autonomous Driving via Reinforcement Learning and Reasoning
- Learning Personalized Driving Styles via Reinforcement Learning from Human Feedback
- DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
- ReCogDrive: A Reinforced Cognitive Framework for End-to-End Autonomous Driving
- Enhancing End-to-End Autonomous Driving with Latent World Model
- DRAMA: An Efficient End-to-end Motion Planner for Autonomous Driving with Mamba
- NuPlan: A closed-loop ML-based planning benchmark for autonomous vehicles
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- APT: Action Expert Pretraining Improves Instruction Generalization of Vision-Language-Action Policies
- Fine-tuning is Not Enough: A Parallel Framework for Collaborative Imitation and Reinforcement Learning in End-to-end Autonomous Driving